Signal analysis and signal treatment are integral parts of all types of Nuclear Magnetic Resonance. In the last ten years, much has been achieved in the development of dimensional spectra. At the same time new NMR techniques such as NMR Imaging and multidimensional spectroscopy have appeared, requiring entirely new methods of signal analysis. Up until now, most NMR texts and reference books limited their presentation of signal processing to a short introduction to the principles of the Fourier Transform, signal convolution, apodisation and noise reduction. To understand the mathematics of the newer signal processing techniques, it was necessary to go back to the primary references in NMR, chemometrics and mathematics journals. The objective of this book is to fill this void by presenting, in a single volume, both the theory and applications of most of these new techniques to Time-Domain, Frequency-Domain and Space-Domain NMR signals. Details are provided on many of the algorithms used and a companion CD-ROM is also included which contains some of the computer programs, either as source code or in executable form. Although it is aimed primarily at NMR users in the medical, industrial and academic fields, it should also interest chemometricians and programmers working with other techniques.

Although originally designed to treat neither spectroscopic nor multidimensional data, FDM was adapted to process NMR spectra of both large and small molecules in liquids in 1D, 2D, 3D, and 4D spectra, with the multidimensional cases requiring important modifications. FDM outperforms the discrete Fourier Transform (DFT) whenever sensitivity is good, the spectrum is sufficiently sparse, the spectral peaks are sharp and Lorentzian, and the extent of the measured time-domain data is short, so that the FT spectrum exhibits line shapes that are distorted only by the time-frequency uncertainty principle. A further study of FDM to perturbations under non-ideal conditions lead to further improvements and finally a multi-dimensional, phase-sensitive, hybrid FDM (HFDM) algorithm that is introduced for the first time. HFDM can enhance the resolution of peaks distorted by time-frequency uncertainty, while still producing a spectrum in which all the original imperfections are present, such that a true noise floor prevents inadvertent contouring of features that look like resonances but that would in fact be buried in noise in any decent spectral estimate. This opens the door to the application of HFDM on NMR data sets with more reliability. Mixture separation is another important area of NMR research and will be the focus of the third chapter. Separating a mixture of signals into its source components is an engaging but challenging problem relevant to research in a wide variety of fields from neurology and medical signal processing to speech recognition systems. The vast majority of NMR spectroscopic data is taken on isolated, purified compounds; yet mixtures are clearly important to tackle. The pharmaceutical industry is a key example of a field that would benefit tremendously from more robust NMR mixture separation techniques. Structural information about promising candidates from the drug design process must be extracted from samples that also contain the target molecule, side products, and impurities. NMR can identify individual samples with respect to their unique fingerprint spectra; however, it has been traditionally poor at handling mixtures. Diffusion ordered spectroscopy (DOSY) has been one of the few NMR techniques that can be used for mixture separation. It uses translational diffusion to effect a partial separation and can be quite powerful when the individual compounds have a distinguishable difference in their diffusion constants. However, the separation problem becomes more difficult when the component molecules have similar chemical functional group and overlap in their NMR spectra. Blind Source Separation (BSS) is a technique that can be used to extract an individual NMR subspectrum from spectra of a number of different mixtures; and--if the molecules do not interact strongly--these individual subspectra adhere closely to those of the corresponding pure compounds under the same conditions. Furthermore, the "mixture spectra" need not be different physical samples: any method that can modify the intensities of all the peaks from a given molecule, in concert, is applicable. Previously a mixture of two components was successfully separated. Recent efforts have uncovered a flaw in the original BSS algorithm that causes behaves erratically for samples with three or more components. This complication will be described in detail, and a newly developed approach-- dubbed "BISI" for Blind Iterative Source Identification--will be introduced that overcomes those previously unforeseen problems.

This reference/text contains the latest signal processing techniques in magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) for more efficient clinical diagnoses-providing ready-to-use algorithms for image segmentation and analysis, reconstruction and visualization, and removal of distortions and artifacts for increased detec

This book is about improving prohibited substances detection using the nuclear quadrupole resonance (NQR) technique at security checkpoints. The book proposes multiple signal processing and analysis techniques for improving detection of dangerous or contraband substances, such as explosives, narcotics, or toxic substances. Also, several hardware solutions are described and implemented in a custom-designed NQR spectrometer. A new approach to NQR signal detection is introduced using artificial intelligence/deep learning techniques. The book will be useful for for researchers and practitioners in the areas of electrical engineering, signal processing and analysis, applied spectroscopy, as well as for security or laboratory equipment manufacturers.

Solid-state nuclear magnetic resonance (SSNMR) is a powerful tool for the study of chemical structure and dynamics. The interactions that manifest in NMR spectra are anisotropic (id est, orientation dependent) in origin, often resulting in inhomogeneous broadening that can yield spectra exceeding circa 250 kHz in breadth; such spectra are often referred to as ultra-wideline (UW) NMR spectra. In many cases, UWNMR spectra cannot be acquired using conventional methods and rectangular radiofrequency (RF) pulses - rather, specialized pulses and techniques must be implemented. Furthermore, the large suite of high-resolution methods based on magic-angle spinning (MAS) are generally unsuitable for the acquisition of UWNMR spectra. Frequency-swept (FS) pulses are amplitude- and phase-modulated pulses that can irradiate NMR frequencies over large bandwidths. One of these is the wideband uniform-rate smooth-truncation (WURST) pulse, which features a linear effective frequency sweep. WURST pulses are used in the WURST-Carr-Purcell/Meiboom-Gill (WURST-CPMG) and broadband adiabatic inversion-cross polarization (BRAIN-CP) pulse sequences, which have been shown to be effective for the acquisition of UWNMR spectra of stationary (static) samples. These sequences have limitations, and to date, their capabilities have not been fully explored, nor have they been described from a theoretical point of view in a comprehensive manner.Signal processing is an integral component of pulsed-Fourier transform NMR spectroscopy, and comprises many protocols for enhancing spectral sensitivity and resolution. There has been much interest in advanced spectral processing methods such as alternate basis transformations (id est, besides Fourier encoding), statistical analyses (e.g., singular value decomposition, SVD, and principal component analysis, PCA), numerical regression (e.g., non-negative least squares), and the use of deep learning and neural networks. Such methodologies are now routinely implemented in imaging and multidimensional solution NMR, but are seldom explored in SSNMR. This thesis describes the development of new methods for the acquisition and advanced processing of conventional, wideline, and UW SSNMR spectra, including: (i) the design of new pulses and pulse sequences with optimal control theory (OCT); (ii) the modification of existing pulse sequences for UWNMR under MAS conditions; (iii) achieving signal enhancements with CPMG pulse sequences via the suppression of weak homonuclear dipolar coupling interactions under static and MAS conditions; (iv) the application of the BRAIN-CP pulse sequence to integer-spin nuclei, along with a thorough theoretical treatment; (v) rapid and robust measurements of longitudinal relaxation time constants (T1) and T1 anisotropies from UWNMR spectra under static conditions; (vi) the development and application of advanced processing methods for resolving UWNMR relaxation data with inverse Laplace transforms; and (vii) the development of a Python library for denoising 1D and 2D NMR spectra with SVD, PCA, and wavelet transforms. It is hoped that these methods will open up the Periodic Table of elements to increasingly routine exploration by SSNMR spectroscopy, aiding researchers in chemistry, biochemistry, materials science, and physics with advanced molecular-level characterization of their materials.